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--- |
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annotations_creators: |
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- no-annotation |
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license: other |
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source_datasets: |
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- original |
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task_categories: |
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- time-series-forecasting |
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task_ids: |
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- univariate-time-series-forecasting |
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- multivariate-time-series-forecasting |
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pretty_name: Chronos datasets |
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dataset_info: |
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- config_name: dominick |
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features: |
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- name: id |
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dtype: string |
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dtype: int64 |
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- name: train |
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num_bytes: 477140250 |
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num_examples: 100014 |
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download_size: 60199910 |
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dataset_size: 477140250 |
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homepage: https://www.chicagobooth.edu/research/kilts/research-data/dominicks |
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- config_name: electricity_15min |
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features: |
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- name: id |
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dtype: string |
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- name: timestamp |
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sequence: timestamp[ms] |
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sequence: float64 |
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splits: |
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- name: train |
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num_bytes: 670989988 |
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num_examples: 370 |
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download_size: 284497403 |
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dataset_size: 670989988 |
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license: CC BY 4.0 |
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homepage: https://archive.ics.uci.edu/dataset/321/electricityloaddiagrams20112014 |
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- config_name: ercot |
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features: |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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num_examples: 8 |
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download_size: 14504261 |
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- config_name: exchange_rate |
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features: |
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dtype: string |
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sequence: float32 |
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splits: |
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- name: train |
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num_examples: 8 |
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download_size: 401501 |
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license: MIT |
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homepage: https://github.com/laiguokun/multivariate-time-series-data/tree/master/exchange_rate |
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- config_name: m4_daily |
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features: |
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num_bytes: 160504176 |
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num_examples: 4227 |
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download_size: 65546675 |
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dataset_size: 160504176 |
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homepage: https://github.com/Mcompetitions/M4-methods |
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- config_name: m4_hourly |
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features: |
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dtype: string |
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num_bytes: 5985544 |
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num_examples: 414 |
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download_size: 1336971 |
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dataset_size: 5985544 |
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homepage: https://github.com/Mcompetitions/M4-methods |
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- config_name: m4_monthly |
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features: |
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download_size: 52772258 |
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dataset_size: 181372969 |
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homepage: https://github.com/Mcompetitions/M4-methods |
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- config_name: m4_quarterly |
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features: |
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dtype: string |
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dtype: string |
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splits: |
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num_bytes: 39205397 |
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num_examples: 24000 |
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download_size: 13422579 |
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dataset_size: 39205397 |
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homepage: https://github.com/Mcompetitions/M4-methods |
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- config_name: m4_weekly |
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features: |
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num_examples: 359 |
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download_size: 2556691 |
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dataset_size: 5955806 |
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homepage: https://github.com/Mcompetitions/M4-methods |
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- config_name: m4_yearly |
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features: |
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dtype: string |
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splits: |
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num_bytes: 14410042 |
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num_examples: 23000 |
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download_size: 5488601 |
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dataset_size: 14410042 |
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homepage: https://github.com/Mcompetitions/M4-methods |
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- config_name: m5 |
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features: |
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- name: id |
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dtype: string |
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sequence: timestamp[ms] |
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dtype: string |
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sequence: float32 |
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- name: dept_id |
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dtype: string |
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- name: cat_id |
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dtype: string |
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- name: store_id |
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dtype: string |
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- name: state_id |
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dtype: string |
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splits: |
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num_bytes: 574062630 |
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num_examples: 30490 |
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download_size: 78063286 |
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dataset_size: 574062630 |
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homepage: https://www.kaggle.com/competitions/m5-forecasting-accuracy/rules |
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- config_name: mexico_city_bikes |
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features: |
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- name: id |
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dtype: string |
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splits: |
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num_bytes: 618999406 |
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num_examples: 494 |
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download_size: 103206946 |
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dataset_size: 618999406 |
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homepage: https://ecobici.cdmx.gob.mx/en/open-data/ |
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- config_name: monash_australian_electricity |
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features: |
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- name: id |
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dtype: string |
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num_bytes: 18484319 |
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num_examples: 5 |
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download_size: 16856156 |
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dataset_size: 18484319 |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_car_parts |
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|
features: |
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|
- name: id |
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dtype: string |
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num_examples: 2674 |
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download_size: 70278 |
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dataset_size: 2232790 |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_cif_2016 |
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features: |
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dtype: string |
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num_examples: 72 |
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download_size: 70876 |
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dataset_size: 115096 |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_covid_deaths |
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features: |
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- name: id |
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dtype: string |
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num_bytes: 907326 |
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num_examples: 266 |
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|
download_size: 58957 |
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dataset_size: 907326 |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_electricity_hourly |
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features: |
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- name: id |
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dtype: string |
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num_bytes: 135103443 |
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num_examples: 321 |
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download_size: 31139117 |
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dataset_size: 135103443 |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_electricity_weekly |
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features: |
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num_bytes: 807315 |
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num_examples: 321 |
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download_size: 333563 |
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dataset_size: 807315 |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_fred_md |
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features: |
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num_bytes: 1248369 |
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num_examples: 107 |
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dataset_size: 1248369 |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_hospital |
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features: |
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num_examples: 767 |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_kdd_cup_2018 |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_london_smart_meters |
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features: |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_m1_monthly |
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features: |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_m1_quarterly |
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features: |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_m1_yearly |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_m3_monthly |
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features: |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_m3_quarterly |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: monash_m3_yearly |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
|
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- config_name: monash_nn5_weekly |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
|
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- config_name: monash_pedestrian_counts |
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license: CC BY 4.0 |
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homepage: https://zenodo.org/communities/forecasting |
|
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- config_name: monash_rideshare |
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download_size: 781873 |
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homepage: https://zenodo.org/communities/forecasting |
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homepage: https://zenodo.org/communities/forecasting |
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- config_name: weatherbench_hourly_geopotential |
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features: |
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- name: latitude |
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dtype: float64 |
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- name: longitude |
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dtype: float64 |
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- name: target |
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sequence: float32 |
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- name: level |
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dtype: int64 |
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- name: timestamp |
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sequence: timestamp[ms] |
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- name: subset |
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dtype: string |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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|
num_examples: 26624 |
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|
download_size: 87305564613 |
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|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_hourly_potential_vorticity |
|
|
features: |
|
|
- name: latitude |
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|
dtype: float64 |
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|
- name: longitude |
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dtype: float64 |
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- name: target |
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sequence: float32 |
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- name: level |
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dtype: int64 |
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- name: timestamp |
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sequence: timestamp[ms] |
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dtype: string |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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num_examples: 26624 |
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|
download_size: 92426240043 |
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|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_hourly_relative_humidity |
|
|
features: |
|
|
- name: latitude |
|
|
dtype: float64 |
|
|
- name: longitude |
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dtype: float64 |
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- name: target |
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sequence: float32 |
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- name: level |
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dtype: int64 |
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sequence: timestamp[ms] |
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dtype: string |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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num_examples: 26624 |
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|
download_size: 94728788382 |
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|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_hourly_specific_humidity |
|
|
features: |
|
|
- name: latitude |
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|
dtype: float64 |
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|
- name: longitude |
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dtype: float64 |
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sequence: float32 |
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- name: level |
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dtype: int64 |
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sequence: timestamp[ms] |
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dtype: string |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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num_examples: 26624 |
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|
download_size: 85139896451 |
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|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_hourly_temperature |
|
|
features: |
|
|
- name: latitude |
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|
dtype: float64 |
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|
- name: longitude |
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dtype: float64 |
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- name: target |
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sequence: float32 |
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- name: level |
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dtype: int64 |
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sequence: timestamp[ms] |
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dtype: string |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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num_examples: 26624 |
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|
download_size: 94081539079 |
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|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_hourly_toa_incident_solar_radiation |
|
|
features: |
|
|
- name: latitude |
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|
dtype: float64 |
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- name: longitude |
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dtype: float64 |
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sequence: float32 |
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dtype: float64 |
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sequence: timestamp[ms] |
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dtype: string |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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|
num_examples: 2048 |
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|
download_size: 6057953007 |
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|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_hourly_total_cloud_cover |
|
|
features: |
|
|
- name: latitude |
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|
dtype: float64 |
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- name: longitude |
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dtype: float64 |
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- name: target |
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sequence: float32 |
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- name: level |
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dtype: float64 |
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- name: timestamp |
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sequence: timestamp[ms] |
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- name: subset |
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dtype: string |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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|
num_examples: 2048 |
|
|
download_size: 6628258398 |
|
|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_hourly_total_precipitation |
|
|
features: |
|
|
- name: latitude |
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|
dtype: float64 |
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|
- name: longitude |
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dtype: float64 |
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- name: target |
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sequence: float32 |
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- name: level |
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dtype: float64 |
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- name: timestamp |
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sequence: timestamp[ms] |
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- name: subset |
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dtype: string |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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|
num_examples: 2048 |
|
|
download_size: 6473160755 |
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|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_hourly_u_component_of_wind |
|
|
features: |
|
|
- name: latitude |
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|
dtype: float64 |
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|
- name: longitude |
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dtype: float64 |
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- name: target |
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sequence: float32 |
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- name: level |
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dtype: int64 |
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- name: timestamp |
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sequence: timestamp[ms] |
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dtype: string |
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- name: id |
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dtype: string |
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splits: |
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- name: train |
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num_examples: 26624 |
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|
download_size: 94801498563 |
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|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_hourly_v_component_of_wind |
|
|
features: |
|
|
- name: latitude |
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dtype: float64 |
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sequence: float32 |
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dtype: string |
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splits: |
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- name: train |
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num_examples: 26624 |
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download_size: 94800557482 |
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license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_hourly_vorticity |
|
|
features: |
|
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- name: latitude |
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dtype: float64 |
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splits: |
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num_examples: 26624 |
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|
download_size: 94720960560 |
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|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: weatherbench_weekly |
|
|
features: |
|
|
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dtype: string |
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sequence: timestamp[ms] |
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sequence: float32 |
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dtype: float64 |
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splits: |
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num_bytes: 5656029184 |
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num_examples: 225280 |
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|
download_size: 2243012083 |
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|
dataset_size: 5656029184 |
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|
license: MIT |
|
|
homepage: https://github.com/pangeo-data/WeatherBench |
|
|
- config_name: wiki_daily_100k |
|
|
features: |
|
|
- name: id |
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dtype: string |
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sequence: timestamp[ms] |
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sequence: float64 |
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- name: page_name |
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dtype: string |
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splits: |
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|
num_bytes: 4389782678 |
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|
num_examples: 100000 |
|
|
download_size: 592554033 |
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|
dataset_size: 4389782678 |
|
|
license: CC0 |
|
|
homepage: https://dumps.wikimedia.org/other/pageviews/readme.html |
|
|
- config_name: wind_farms_daily |
|
|
features: |
|
|
- name: id |
|
|
dtype: string |
|
|
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sequence: timestamp[ms] |
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sequence: float64 |
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splits: |
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num_bytes: 1919187 |
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num_examples: 337 |
|
|
download_size: 598834 |
|
|
dataset_size: 1919187 |
|
|
license: CC BY 4.0 |
|
|
homepage: https://zenodo.org/communities/forecasting |
|
|
- config_name: wind_farms_hourly |
|
|
features: |
|
|
- name: id |
|
|
dtype: string |
|
|
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sequence: timestamp[ms] |
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sequence: float64 |
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splits: |
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|
num_bytes: 45917027 |
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|
num_examples: 337 |
|
|
download_size: 12333116 |
|
|
dataset_size: 45917027 |
|
|
license: CC BY 4.0 |
|
|
homepage: https://zenodo.org/communities/forecasting |
|
|
configs: |
|
|
- config_name: dominick |
|
|
data_files: |
|
|
- split: train |
|
|
path: dominick/train-* |
|
|
- config_name: electricity_15min |
|
|
data_files: |
|
|
- split: train |
|
|
path: electricity_15min/train-* |
|
|
- config_name: ercot |
|
|
data_files: |
|
|
- split: train |
|
|
path: ercot/train-* |
|
|
- config_name: exchange_rate |
|
|
data_files: |
|
|
- split: train |
|
|
path: exchange_rate/train-* |
|
|
- config_name: m4_daily |
|
|
data_files: |
|
|
- split: train |
|
|
path: m4_daily/train-* |
|
|
- config_name: m4_hourly |
|
|
data_files: |
|
|
- split: train |
|
|
path: m4_hourly/train-* |
|
|
- config_name: m4_monthly |
|
|
data_files: |
|
|
- split: train |
|
|
path: m4_monthly/train-* |
|
|
- config_name: m4_quarterly |
|
|
data_files: |
|
|
- split: train |
|
|
path: m4_quarterly/train-* |
|
|
- config_name: m4_weekly |
|
|
data_files: |
|
|
- split: train |
|
|
path: m4_weekly/train-* |
|
|
- config_name: m4_yearly |
|
|
data_files: |
|
|
- split: train |
|
|
path: m4_yearly/train-* |
|
|
- config_name: m5 |
|
|
data_files: |
|
|
- split: train |
|
|
path: m5/train-* |
|
|
- config_name: mexico_city_bikes |
|
|
data_files: |
|
|
- split: train |
|
|
path: mexico_city_bikes/train-* |
|
|
- config_name: monash_australian_electricity |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_australian_electricity/train-* |
|
|
- config_name: monash_car_parts |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_car_parts/train-* |
|
|
- config_name: monash_cif_2016 |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_cif_2016/train-* |
|
|
- config_name: monash_covid_deaths |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_covid_deaths/train-* |
|
|
- config_name: monash_electricity_hourly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_electricity_hourly/train-* |
|
|
- config_name: monash_electricity_weekly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_electricity_weekly/train-* |
|
|
- config_name: monash_fred_md |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_fred_md/train-* |
|
|
- config_name: monash_hospital |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_hospital/train-* |
|
|
- config_name: monash_kdd_cup_2018 |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_kdd_cup_2018/train-* |
|
|
- config_name: monash_london_smart_meters |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_london_smart_meters/train-* |
|
|
- config_name: monash_m1_monthly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_m1_monthly/train-* |
|
|
- config_name: monash_m1_quarterly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_m1_quarterly/train-* |
|
|
- config_name: monash_m1_yearly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_m1_yearly/train-* |
|
|
- config_name: monash_m3_monthly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_m3_monthly/train-* |
|
|
- config_name: monash_m3_quarterly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_m3_quarterly/train-* |
|
|
- config_name: monash_m3_yearly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_m3_yearly/train-* |
|
|
- config_name: monash_nn5_weekly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_nn5_weekly/train-* |
|
|
- config_name: monash_pedestrian_counts |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_pedestrian_counts/train-* |
|
|
- config_name: monash_rideshare |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_rideshare/train-* |
|
|
- config_name: monash_saugeenday |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_saugeenday/train-* |
|
|
- config_name: monash_temperature_rain |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_temperature_rain/train-* |
|
|
- config_name: monash_tourism_monthly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_tourism_monthly/train-* |
|
|
- config_name: monash_tourism_quarterly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_tourism_quarterly/train-* |
|
|
- config_name: monash_tourism_yearly |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_tourism_yearly/train-* |
|
|
- config_name: monash_traffic |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_traffic/train-* |
|
|
- config_name: monash_weather |
|
|
data_files: |
|
|
- split: train |
|
|
path: monash_weather/train-* |
|
|
- config_name: nn5 |
|
|
data_files: |
|
|
- split: train |
|
|
path: nn5/train-* |
|
|
- config_name: solar |
|
|
data_files: |
|
|
- split: train |
|
|
path: solar/train-* |
|
|
- config_name: solar_1h |
|
|
data_files: |
|
|
- split: train |
|
|
path: solar_1h/train-* |
|
|
- config_name: taxi_1h |
|
|
data_files: |
|
|
- split: train |
|
|
path: taxi_1h/train-* |
|
|
- config_name: taxi_30min |
|
|
data_files: |
|
|
- split: train |
|
|
path: taxi_30min/train-* |
|
|
- config_name: uber_tlc_daily |
|
|
data_files: |
|
|
- split: train |
|
|
path: uber_tlc_daily/train-* |
|
|
- config_name: uber_tlc_hourly |
|
|
data_files: |
|
|
- split: train |
|
|
path: uber_tlc_hourly/train-* |
|
|
- config_name: ushcn_daily |
|
|
data_files: |
|
|
- split: train |
|
|
path: ushcn_daily/train-* |
|
|
- config_name: weatherbench_daily |
|
|
data_files: |
|
|
- split: train |
|
|
path: weatherbench_daily/train-* |
|
|
- config_name: weatherbench_hourly_10m_u_component_of_wind |
|
|
data_files: |
|
|
- split: train |
|
|
path: weatherbench_hourly/10m_u_component_of_wind/train-* |
|
|
- config_name: weatherbench_hourly_10m_v_component_of_wind |
|
|
data_files: |
|
|
- split: train |
|
|
path: weatherbench_hourly/10m_v_component_of_wind/train-* |
|
|
- config_name: weatherbench_hourly_2m_temperature |
|
|
data_files: |
|
|
- split: train |
|
|
path: weatherbench_hourly/2m_temperature/train-* |
|
|
- config_name: weatherbench_hourly_geopotential |
|
|
data_files: |
|
|
- split: train |
|
|
path: weatherbench_hourly/geopotential/train-* |
|
|
- config_name: weatherbench_hourly_potential_vorticity |
|
|
data_files: |
|
|
- split: train |
|
|
path: weatherbench_hourly/potential_vorticity/train-* |
|
|
- config_name: weatherbench_hourly_relative_humidity |
|
|
data_files: |
|
|
- split: train |
|
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path: weatherbench_hourly/relative_humidity/train-* |
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- config_name: weatherbench_hourly_specific_humidity |
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data_files: |
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- split: train |
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path: weatherbench_hourly/specific_humidity/train-* |
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- config_name: weatherbench_hourly_temperature |
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data_files: |
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- split: train |
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path: weatherbench_hourly/temperature/train-* |
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|
- config_name: weatherbench_hourly_toa_incident_solar_radiation |
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data_files: |
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- split: train |
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path: weatherbench_hourly/toa_incident_solar_radiation/train-* |
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- config_name: weatherbench_hourly_total_cloud_cover |
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data_files: |
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- split: train |
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path: weatherbench_hourly/total_cloud_cover/train-* |
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|
- config_name: weatherbench_hourly_total_precipitation |
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data_files: |
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- split: train |
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path: weatherbench_hourly/total_precipitation/train-* |
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|
- config_name: weatherbench_hourly_u_component_of_wind |
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data_files: |
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- split: train |
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path: weatherbench_hourly/u_component_of_wind/train-* |
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|
- config_name: weatherbench_hourly_v_component_of_wind |
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|
data_files: |
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|
- split: train |
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|
path: weatherbench_hourly/v_component_of_wind/train-* |
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|
- config_name: weatherbench_hourly_vorticity |
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data_files: |
|
|
- split: train |
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|
path: weatherbench_hourly/vorticity/train-* |
|
|
- config_name: weatherbench_weekly |
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|
data_files: |
|
|
- split: train |
|
|
path: weatherbench_weekly/train-* |
|
|
- config_name: wiki_daily_100k |
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|
data_files: |
|
|
- split: train |
|
|
path: wiki_daily_100k/train-* |
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|
- config_name: wind_farms_daily |
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|
data_files: |
|
|
- split: train |
|
|
path: wind_farms_daily/train-* |
|
|
- config_name: wind_farms_hourly |
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|
data_files: |
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|
- split: train |
|
|
path: wind_farms_hourly/train-* |
|
|
--- |
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# Chronos datasets |
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|
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Time series datasets used for training and evaluation of the [Chronos](https://github.com/amazon-science/chronos-forecasting) forecasting models. |
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|
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Note that some Chronos datasets (`ETTh`, `ETTm`, `brazilian_cities_temperature` and `spanish_energy_and_weather`) that rely on a custom builder script are available in the companion repo [`autogluon/chronos_datasets_extra`](https://huggingface.co/datasets/autogluon/chronos_datasets_extra). |
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See the [paper](https://arxiv.org/abs/2403.07815) for more information. |
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## Data format and usage |
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All datasets satisfy the following high-level schema: |
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- Each dataset row corresponds to a single (univariate or multivariate) time series. |
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- There exists one column with name `id` and type `string` that contains the unique identifier of each time series. |
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|
- There exists one column of type `Sequence` with dtype `timestamp[ms]`. This column contains the timestamps of the observations. Timestamps are guaranteed to have a regular frequency that can be obtained with [`pandas.infer_freq`](https://pandas.pydata.org/docs/reference/api/pandas.infer_freq.html). |
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- There exists at least one column of type `Sequence` with numeric (`float`, `double`, or `int`) dtype. These columns can be interpreted as target time series. |
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- For each row, all columns of type `Sequence` have same length. |
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- Remaining columns of types other than `Sequence` (e.g., `string` or `float`) can be interpreted as static covariates. |
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|
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|
Datasets can be loaded using the 🤗 [`datasets`](https://huggingface.co/docs/datasets/en/index) library |
|
|
```python |
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import datasets |
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|
ds = datasets.load_dataset("autogluon/chronos_datasets", "m4_daily", split="train") |
|
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ds.set_format("numpy") # sequences returned as numpy arrays |
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|
``` |
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|
|
> **NOTE:** The `train` split of all datasets contains the full time series and has no relation to the train/test split used in the Chronos paper. |
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|
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|
Example entry in the `m4_daily` dataset |
|
|
```python |
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>>> ds[0] |
|
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{'id': 'T000000', |
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|
'timestamp': array(['1994-03-01T12:00:00.000', '1994-03-02T12:00:00.000', |
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'1994-03-03T12:00:00.000', ..., '1996-12-12T12:00:00.000', |
|
|
'1996-12-13T12:00:00.000', '1996-12-14T12:00:00.000'], |
|
|
dtype='datetime64[ms]'), |
|
|
'target': array([1017.1, 1019.3, 1017. , ..., 2071.4, 2083.8, 2080.6], dtype=float32), |
|
|
'category': 'Macro'} |
|
|
``` |
|
|
|
|
|
### Converting to pandas |
|
|
We can easily convert data in such format to a long format data frame |
|
|
```python |
|
|
def to_pandas(ds: datasets.Dataset) -> "pd.DataFrame": |
|
|
"""Convert dataset to long data frame format.""" |
|
|
sequence_columns = [col for col in ds.features if isinstance(ds.features[col], datasets.Sequence)] |
|
|
return ds.to_pandas().explode(sequence_columns).infer_objects() |
|
|
``` |
|
|
Example output |
|
|
```python |
|
|
>>> print(to_pandas(ds).head()) |
|
|
id timestamp target category |
|
|
0 T000000 1994-03-01 12:00:00 1017.1 Macro |
|
|
1 T000000 1994-03-02 12:00:00 1019.3 Macro |
|
|
2 T000000 1994-03-03 12:00:00 1017.0 Macro |
|
|
3 T000000 1994-03-04 12:00:00 1019.2 Macro |
|
|
4 T000000 1994-03-05 12:00:00 1018.7 Macro |
|
|
``` |
|
|
|
|
|
|
|
|
### Dealing with large datasets |
|
|
Note that some datasets, such as subsets of WeatherBench, are extremely large (~100GB). To work with them efficiently, we recommend either loading them from disk (files will be downloaded to disk, but won't be all loaded into memory) |
|
|
```python |
|
|
ds = datasets.load_dataset("autogluon/chronos_datasets", "weatherbench_daily", keep_in_memory=False, split="train") |
|
|
``` |
|
|
or, for the largest datasets like `weatherbench_hourly_temperature`, reading them in streaming format (chunks will be downloaded one at a time) |
|
|
```python |
|
|
ds = datasets.load_dataset("autogluon/chronos_datasets", "weatherbench_hourly_temperature", streaming=True, split="train") |
|
|
``` |
|
|
|
|
|
## License |
|
|
Different datasets available in this collection are distributed under different open source licenses. Please see `ds.info.license` and `ds.info.homepage` for each individual dataset. |
|
|
|
|
|
## Citation |
|
|
|
|
|
If you find these datasets useful for your research, please consider citing the associated paper: |
|
|
```markdown |
|
|
@article{ansari2024chronos, |
|
|
author = {Ansari, Abdul Fatir and Stella, Lorenzo and Turkmen, Caner and Zhang, Xiyuan and Mercado, Pedro and Shen, Huibin and Shchur, Oleksandr and Rangapuram, Syama Syndar and Pineda Arango, Sebastian and Kapoor, Shubham and Zschiegner, Jasper and Maddix, Danielle C. and Wang, Hao and Mahoney, Michael W. and Torkkola, Kari and Gordon Wilson, Andrew and Bohlke-Schneider, Michael and Wang, Yuyang}, |
|
|
title = {Chronos: Learning the Language of Time Series}, |
|
|
journal = {arXiv preprint arXiv:2403.07815}, |
|
|
year = {2024} |
|
|
} |
|
|
``` |
|
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|
